Prior to the background of the invention being set forth, it may be helpful to set forth definitions of certain terms that will be used hereinafter.
The term “sentiment analysis” as used herein is defined as the use of natural language processing, text analysis and computational linguistics to identify the attitude, opinions and/or emotions that an author of a text expresses regarding specific entities or subjects.
The term “sentiment-entity association” as used herein is referred to the process of associating a sentiment to an entity that is presented in a text. In case of a plurality of entities, the sentiment-entity association process will associate a sentiment with its corresponding entity.
The sentiment, the “tone” of political discourse, can be almost as influential as the substantive content of the text. The basic question asked in this context is whether the author describes the target of the text in a good, bad or neutral way. Some known solutions of addressing the sentiment-entity association challenge in a political discourse tend to use a proximity measure. One approach, common in online systems, uses the co-occurrence of the sentiment expression and an entity mentioned in the same sentence as a sufficient cue for associating them. Yet, even the sentence level of analysis might be too broad, as some sentences refer to more than one entity; hence further narrowing of the level of analysis might be preferred.
It would be advantageous to be able to associate sentiment to entities presented in a text in a more structured manner that will increase the correctness of the association.
In some cases, the sentiment is difficult to be assessed. This may happen when the opinion of the reader further affect the way an entity is being judged. The author may present an entity by providing his or her opinion in regards to the outcome of an entity's deeds, but the specific outcome can be appreciated differently by two different readers.
Therefore, it would be further advantageous to find another indicator for evaluating entities in a text that is less prone to reader or author perspective.